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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Updated: May 24, 2026

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
07:47

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

AI in multi-omics analysis in AMR.

Neelja Singhal1, Manish Kumar1

  • 1Department of Biophysics, University of Delhi South Campus, New Delhi, India.

Progress in Molecular Biology and Translational Science
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) is crucial for analyzing complex multi-omics data to understand antimicrobial resistance (AMR). AI helps uncover patterns in genetic variations, protein expressions, and metabolic adaptations to combat this global health threat.

Keywords:
Antimicrobial resistanceArtificial intelligenceDeep learningMachine learningMulti-omics

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Infectious Disease Research

Background:

  • Antimicrobial resistance (AMR) is a critical global health challenge, threatening infectious disease control.
  • High-throughput multi-omics technologies (genomics, transcriptomics, proteomics, metabolomics) generate vast, complex datasets.
  • Traditional methods struggle with the scale and heterogeneity of multi-omics data for AMR research.

Purpose of the Study:

  • To provide a comprehensive overview of Artificial Intelligence (AI) applications in multi-omics analysis for AMR.
  • To explore conceptual foundations, methodological advancements, and applications of AI in AMR research.
  • To examine the translational implications, limitations, and future directions of AI-driven multi-omics strategies.

Main Methods:

  • Review and synthesis of current literature on AI and multi-omics in AMR.
  • Analysis of AI techniques (machine learning, deep learning) for integrating diverse omics data.
  • Discussion of representative applications and case studies in AMR research.

Main Results:

  • AI, including machine learning and deep learning, effectively integrates multi-omics data to model complex interactions in AMR.
  • AI provides predictive and mechanistic insights into AMR development, surpassing traditional analytical methods.
  • Significant applications demonstrate AI's potential in identifying AMR patterns and drivers.

Conclusions:

  • AI is essential for extracting meaningful insights from high-dimensional multi-omics data to combat AMR.
  • Addressing limitations through data standardization, validation, and collaboration is key to realizing AI's full potential.
  • AI-driven multi-omics approaches offer promising strategies for future AMR research and intervention.